Disease-related Variants Research Articles

Transformers significantly improve splice site prediction

Mutations that affect RNA splicing significantly impact human diversity and disease. Here we present a method using transformers, a type of machine learning model, to detect splicing from raw 45,000-nucleotide sequences. We generate embeddings with residual neural networks and apply hard attention to select splice site candidates, enabling efficient training on long sequences. Our method surpasses the leading tool, SpliceAI, in detecting splice sites in GENCODE and ENSEMBL annotations. Using extensive RNA sequencing data from an Icelandic cohort of 17,848 individuals and the Genotype-Tissue Expression (GTEx) project, our method demonstrates superior performance in detecting splice junctions compared to SpliceAI-10k (PR-AUC = 0.834 vs. PR-AUC = 0.820) and is more effective at identifying disease-related splice variants in ClinVar (PR-AUC = 0.997 vs. PR-AUC = 0.996). These advancements hold promise for improving genetic research and clinical diagnostics, potentially leading to better understanding and treatment of splicing-related diseases.

The evolution of dbSNP: 25 years of impact in genomic research.

The Single Nucleotide Polymorphism Database (dbSNP), established in 1998 by the National Center for Biotechnology Information (NCBI), has been a critical resource in genomics for cataloging small genetic variations. Originally focused on single nucleotide polymorphisms (SNPs), dbSNP has since expanded to include a variety of genetic variants, playing a key role in genome-wide association studies (GWAS), population genetics, pharmacogenomics, and cancer research. Over 25 years, dbSNP has grown to include more than 4.4 billion submitted SNPs and 1.1 billion unique reference SNPs, providing essential data for identifying disease-related genetic variants and studying human diversity. Integrating large-scale projects like 1000 Genomes, gnomAD, TOPMed, and ALFA has expanded dbSNP's catalog of human genetic variation, increasing its usefulness for research and clinical applications. Keeping up with advancements such as next-generation sequencing and cloud-based infrastructure, dbSNP remains a cornerstone of genetic research supporting continued discoveries in precision medicine and population genomics. DATABASE URL: https://www.ncbi.nlm.nih.gov/snp.

Mutational and Clinical Spectrum of the PAX6 Gene in Bulgarian Patients with Aniridia

The PAX6 gene has highly conservative structure and developmentally essential function and is expressed in structurally different organs and systems. Haploinsufficiency of the PAX6 gene product leads to aniridia and aniridia-like phenotypes. A total of 9 patients and 115 controls were examined with the use of a variety of molecular-genetics methods (SNP array, Sanger sequencing, NGS, WES) and in collaboration with different clinicians. Two pathogenic CNVs, three potentially pathogenic variants and two nonsynonymous SNVs in the PAX6 gene were identified: two out of the three potentially pathogenic variants have not been previously reported. The heterogeneous nature of aniridia and the variable spectrum of the PAX6 mutations were confirmed by our study. Approximately 75% of the registered cases in the Bulgarian population were studied and five disease-related variants were discovered. The small number of polymorphic variants in the controls supports the highly conserved role and nature of the PAX6 and its product.

DOCK8 deficiency due to a deep intronic variant in two kindreds with hyper-IgE syndrome

Dedicator of cytokinesis 8 (DOCK8) deficiency underlies the majority of cases of patients with autosomal recessive form of the hyper-immunoglobulin E syndrome (HIES). Most DOCK8 mutations involve deletions and splice junction mutations that abrogate protein expression. However, a few patients whose presentation is reminiscent of DOCK8 deficiency have no identifiable mutations. Using Whole Exome Sequencing (WES), we identified a deep intronic homozygous DOCK8 variant located in intron 36 (c.4626 + 76 A > G) in two unrelated patients with features of HIES that resulted in an in-frame 75 base pair intronic sequence insertion in DOCK8 cDNA, resulting in a premature stop codon (p.S1542ins6Ter). This variant resulted in variable decrease in DOCK8 expression that was associated with impaired T cell receptor-triggered actin polymerization, decreased IL-6-induced STAT3 phosphorylation, reduced expression of the Th17 cell markers CCR6 and IL-17, and higher frequencies of GATA3+ T cells indicative of Th2 skewing. Our approach extends the reach of WES in identifying disease-related intronic variants. It highlights the role of non-coding mutations in immunodeficiency disorders, including DOCK8 deficiency, and emphasizes the need to explore these mutations in unexplained inborn errors of immunity.

Prioritizing disease-related rare variants by integrating gene expression data.

Rare variants, comprising the vast majority of human genetic variations, are likely to have more deleterious impact in the context of human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

DNA breathing integration with deep learning foundational model advances genome-wide binding prediction of human transcription factors.

It was previously shown that DNA breathing, thermodynamic stability, as well as transcriptional activity and transcription factor (TF) bindings are functionally correlated. To ascertain the precise relationship between TF binding and DNA breathing, we developed the multi-modal deep learning model EPBDxDNABERT-2, which is based on the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA dynamics model. To train our EPBDxDNABERT-2, we used chromatin immunoprecipitation sequencing (ChIP-Seq) data comprising 690 ChIP-seq experimental results encompassing 161 distinct TFs and 91 human cell types. EPBDxDNABERT-2 significantly improves the prediction of over 660 TF-DNA, with an increase in the area under the receiver operating characteristic (AUROC) metric of up to 9.6% when compared to the baseline model that does not leverage DNA biophysical properties. We expanded our analysis to in vitro high-throughput Systematic Evolution of Ligands by Exponential enrichment (HT-SELEX) dataset of 215 TFs from 27 families, comparing EPBD with established frameworks. The integration of the DNA breathing features with DNABERT-2 foundational model, greatly enhanced TF-binding predictions. Notably, EPBDxDNABERT-2, trained on a large-scale multi-species genomes, with a cross-attention mechanism, improved predictive power shedding light on the mechanisms underlying disease-related non-coding variants discovered in genome-wide association studies.

Liquid-liquid phase separation of alpha-synuclein increases the structural variability of fibrils formed during amyloid aggregation.

Protein liquid-liquid phase separation (LLPS) is a rapidly emerging field of study on biomolecular condensate formation. In recent years, this phenomenon has been implicated in the process of amyloid fibril formation, serving as an intermediate step between the native protein transition into their aggregated state. The formation of fibrils via LLPS has been demonstrated for a number of proteins related to neurodegenerative disorders, as well as other amyloidoses. Despite the surge in amyloid-related LLPS studies, the influence of protein condensate formation on the end-point fibril characteristics is still far from fully understood. In this work, we compare alpha-synuclein aggregation under different conditions, which promote or negate its LLPS and examine the differences between the formed aggregates. We show that alpha-synuclein phase separation generates a wide variety of assemblies with distinct secondary structures and morphologies. The LLPS-induced structures also possess higher levels of toxicity to cells, indicating that biomolecular condensate formation may be a critical step in the appearance of disease-related fibril variants.

Acute sleep deprivation in mice generates protein pathology consistent with neurodegenerative diseases.

Insufficient or disturbed sleep is strongly associated with adverse health conditions, including various neurodegenerative disorders. While the relationship between sleep and neurodegenerative disease is likely bidirectional, sleep disturbances often predate the onset of other hallmark clinical symptoms. Neuronal waste clearance is significantly more efficient during sleep; thus, disturbed sleep may lead to the accumulation of neuronal proteins that underlie neurodegenerative diseases. Key pathological features of neurodegenerative diseases include an accumulation of misfolded or misprocessed variants of amyloid beta (Aβ), tau, alpha synuclein (α-syn), and TarDNA binding protein 43 (TDP-43). While the presence of fibrillar protein aggregates of these neuronal proteins are characteristic of neurodegenerative diseases, the presence of small soluble toxic oligomeric variants of these different proteins likely precedes the formation of the hallmark aggregates. We hypothesized that sleep deprivation would lead to accumulation of toxic oligomeric variants of Aβ, tau, α-syn, and TDP-43 in brain tissue of wild-type mice. Adult mice were subjected to 6 h of sleep deprivation (zeitgeber 0-6) for 5 consecutive days or were left undisturbed as controls. Following sleep deprivation, brains were collected, and protein pathology was assessed in multiple brain regions using an immunostain panel of reagents selectively targeting neurodegenerative disease-related variants of Aβ, tau, α-syn, and TDP-43. Overall, sleep deprivation elevated levels of all protein variants in at least one of the brain regions of interest. The reagent PDTDP, targeting a TDP-43 variant present in Parkinson's disease, was elevated throughout the brain. The cortex, caudoputamen, and corpus callosum brain regions showed the highest accumulation of pathology following sleep deprivation. These data provide a direct mechanistic link between sleep deprivation, and the hallmark protein pathologies of neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases.

Molecular Genetic Diagnosis with Targeted Next Generation Sequencing in a Cohort of Turkish Osteogenesis Imperfecta Patients and their Genotype-phenotype Correlation

Osteogenesis imperfecta (OI) consists of a group of phenotypically and genetically heterogeneous connective tissue disorders that share similar skeletal anomalies causing bone fragility and deformation. The aim was to investigate the molecular genetic etiology and determine the relationship between genotype and phenotype in OI patients using targeted next-generation sequencing (NGS). A targeted NGS analysis panel (Illumina TruSight One) containing genes involved in collagen/bone synthesis was performed on the Illumina Nextseq550 platform in patients with a confirmed diagnosis of OI. Fifty-six patients (female/male: 25/31) from 46 different families were included. Consanguinity was noted in 15 (32.6%) families. Based on Sillence classification 18 (33.1%) were type 1 OI, 1 (1.7%) type 2, 26 (46.4%) type 3 and 11 (19.6%) type 4. Median body weight was -1.1 (-6.8, - 2.5) standard deviation scores (SDS), and height was -2.3 (-7.6, - 1.2) SDS. Bone deformity affected 30 (53.5%), while 31 (55.4%) were evaluated as mobile. Thirty-six (60.7%) had blue sclera, 13 (23.2%) had scoliosis, 12 (21.4%) had dentinogenesis imperfecta (DI), and 2 (3.6%) had hearing loss. Disease-causing variants in COL1A1 and COL1A2 were found in 24 (52.1%) and 6 (13%) families, respectively. In 8 (17.3%) of the remaining 16 (34.7%) families, the NGS panel revealed disease-causing variants in three different genes (FKBP10, SERPINF1, and P3H1). Nine (23.6%) of the variants detected by NGS panel had not previously been reported and were also classified as pathogenic based on American College of Medical Genetics guidelines pathogenity scores. In ten (21.7%) families, a disease-related variant was not found in any of the 13 OI genes on the panel. Genetic etiology was found in 38 (82.6%) of 46 families by targeted NGS analysis. Furthermore, nine new variants were identified in known OI genes which were classified as pathogenic by standard guidelines.

Destabilization and Degradation of a Disease-Linked PGM1 Protein Variant.

PGM1-linked congenital disorder of glycosylation (PGM1-CDG) is an autosomal recessive disease characterized by several phenotypes, some of which are life-threatening. Research focusing on the disease-related variants of the α-D-phosphoglucomutase 1 (PGM1) protein has shown that several are insoluble in vitro and expressed at low levels in patient fibroblasts. Due to these observations, we hypothesized that some disease-linked PGM1 protein variants are structurally destabilized and subject to protein quality control (PQC) and rapid intracellular degradation. Employing yeast-based assays, we show that a disease-associated human variant, PGM1 L516P, is insoluble, inactive, and highly susceptible to ubiquitylation and rapid degradation by the proteasome. In addition, we show that PGM1 L516P forms aggregates in S. cerevisiae and that both the aggregation pattern and the abundance of PGM1 L516P are chaperone-dependent. Finally, using computational methods, we perform saturation mutagenesis to assess the impact of all possible single residue substitutions in the PGM1 protein. These analyses identify numerous missense variants with predicted detrimental effects on protein function and stability. We suggest that many disease-linked PGM1 variants are subject to PQC-linked degradation and that our in silico site-saturated data set may assist in the mechanistic interpretation of PGM1 variants.

Estimation of the Age of the Kashubian-Specific Pathogenic NPHS2 Variant Responsible for Hereditary Steroid-Resistant Nephrotic Syndrome Points to Its Recent Local Origin

Steroid-resistant nephrotic syndrome (SRNS) is a highly heterogenic kidney disorder resulting from genetic abnormalities or immune system dysfunction affecting the establishment and maintenance of the glomerular filtration barrier. The most common cause of genetic SRNS is biallelic pathogenic variants in NPHS2 gene, especially in individuals with an infantile or childhood onset. The type of the NPHS2 defect implies the course of the disease and the stage of its onset and differs across populations. In a cohort of Polish patients with SRNS, a unique profile of the disease-related NPHS2 variants was identified in patients from northern Poland inhabited by Kashubs, a minority West-Slavic ethnic group known for a local increase of the frequency of several pathogenic variants. Among Kashubian families, the compound heterozygotes c.686G>A/c.1032delT and a single c.1032delT homozygote were the only underlying cause of SRNS. The restricted, Kashubian-only pattern of c.1032delT occurrence, suggesting the founder effect, prompted us to conduct a detailed analysis of its haplotype background to estimate the age of the c.1032delT origin. Eight Kashubian SRNS families were genotyped using the Infinium Global Screening Array-24. The haplotype background analysis was performed using an in-house pipeline designed to solve the phase of the heterozygous genotype data. The age of the c.1032delT mutation was calculated using the gamma method based on the genetic length of ancestral haplotypes shared between two or more individuals carrying this variant. The results of our study indicated a very recent origin of the c.1032delT mutation (~240 years). Genetic screening performed in the general Polish population control corroborates the assumption that the mutation occurred on the specific Kashubian haplotype background. The identification of ancestry-specific Kashubian pathogenic variant can help to develop effective screening and diagnostic strategies as a part of personalized medicine approach in the region.

DVA: predicting the functional impact of single nucleotide missense variants

BackgroundIn the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants.ResultsWe present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein–protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods.ConclusionsDVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Apoptosis, a useful marker in the management of hot-phase cardiomyopathy?

'Hot phases', characterized by chest pain and troponin release, may represent the first clinical presentation of arrhythmogenic cardiomyopathies. Differential diagnosis with acute myocarditis is an unmet challenge for the clinicians. We sought to investigate histological and genetic features in patients with cardiomyopathy presenting with hot phases. We evaluated a case series of consecutive patients hospitalized for suspected 'hot-phase cardiomyopathy' in two Italian centres from June 2017 to March 2022 (median follow-up 18 months) that underwent both endomyocardial biopsy (EMB) and genetic testing. Apoptosis was confirmed with TUNEL assay. Among the 17 enrolled patients (mean age 34 ± 15 years, 76% male), only six patients (35%) presented standard histological and immunohistochemical markers for significant cardiac inflammation at EMB. Conversely, apoptosis was found in 13 patients (77%). Genetic testing was positive for a pathogenic/likely pathogenic (P/LP) variant in genes involved in cardiomyopathies (most frequently in DSP) in eight patients (48%), rising to 62% among patients with apoptosis on EMB. Notably, all patients without apoptosis tested negative for P/LP disease-related variants. Left ventricular ejection fraction was lower in patients showing apoptosis at EMB compared to those without (p = 0.003). Apoptosis, rather than significant inflammation, was mostly prevalent in this case series of patients with 'hot-phase' presentation, especially in carriers of variants in cardiomyopathy-related genes. Detecting apoptosis on EMB might guide clinicians in performing genetic testing and in more tailored therapeutic choices in 'hot-phase cardiomyopathy'.

Traumatic Brain Injury in Mice Generates Early-Stage Alzheimer’s Disease Related Protein Pathology that Correlates with Neurobehavioral Deficits

Traumatic brain injury (TBI) increases the long-term risk of neurodegenerative diseases, including Alzheimer’s disease (AD). Here, we demonstrate that protein variant pathology generated in brain tissue of an experimental TBI mouse model is similar to protein variant pathology observed during early stages of AD, and that subacute accumulation of AD associated variants of amyloid beta (Aβ) and tau in the TBI mouse model correlated with behavioral deficits. Male C57BL/6 mice were subjected to midline fluid percussion injury or to sham injury, after which sensorimotor function (rotarod, neurological severity score), cognitive deficit (novel object recognition), and affective deficits (elevated plus maze, forced swim task) were assessed post-injury (DPI). Protein pathology at 7, 14, and 28 DPI was measured in multiple brain regions using an immunostain panel of reagents selectively targeting different neurodegenerative disease-related variants of Aβ, tau, TDP-43, and alpha-synuclein. Overall, TBI resulted in sensorimotor deficits and accumulation of AD-related protein variant pathology near the impact site, both of which returned to sham levels by 14 DPI. Individual mice, however, showed persistent behavioral deficits and/or accumulation of toxic protein variants at 28 DPI. Behavioral outcomes of each mouse were correlated with levels of seven different protein variants in ten brain regions at specific DPI. Out of 21 significant correlations between protein variant levels and behavioral deficits, 18 were with variants of Aβ or tau. Correlations at 28 DPI were all between a single Aβ or tau variant, both of which are strongly associated with human AD cases. These data provide a direct mechanistic link between protein pathology resulting from TBI and the hallmarks of AD.

The genetic landscape of mitochondrial diseases in the next-generation sequencing era: a Portuguese cohort study.

Introduction: Rare disorders that are genetically and clinically heterogeneous, such as mitochondrial diseases (MDs), have a challenging diagnosis. Nuclear genes codify most proteins involved in mitochondrial biogenesis, despite all mitochondria having their own DNA. The development of next-generation sequencing (NGS) technologies has revolutionized the understanding of many genes involved in the pathogenesis of MDs. In this new genetic era, using the NGS approach, we aimed to identify the genetic etiology for a suspected MD in a cohort of 450 Portuguese patients. Methods: We examined 450 patients using a combined NGS strategy, starting with the analysis of a targeted mitochondrial panel of 213 nuclear genes, and then proceeding to analyze the whole mitochondrial DNA. Results and Discussion: In this study, we identified disease-related variants in 134 (30%) analyzed patients, 88 with nuclear DNA (nDNA) and 46 with mitochondrial DNA (mtDNA) variants, most of them being pediatric patients (66%), of which 77% were identified in nDNA and 23% in mtDNA. The molecular analysis of this cohort revealed 72 already described pathogenic and 20 novel, probably pathogenic, variants, as well as 62 variants of unknown significance. For this cohort of patients with suspected MDs, the use of a customized gene panel provided a molecular diagnosis in a timely and cost-effective manner. Patients who cannot be diagnosed after this initial approach will be further selected for whole-exome sequencing. Conclusion: As a national laboratory for the study and research of MDs, we demonstrated the power of NGS to achieve a molecular etiology, expanding the mutational spectrum and proposing accurate genetic counseling in this group of heterogeneous diseases without therapeutic options.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Individuals WODCI at baseline scan. Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Elucidating the gating mechanisms of disease-related GluN1 Y647 variants

Elucidating the gating mechanisms of disease-related GluN1 Y647 variants

Combining Off-flow, a Nextflow-coded program, and whole genome sequencing reveals unintended genetic variation in CRISPR/Cas-edited iPSCs

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas nucleases and human induced pluripotent stem cell (iPSC) technology can reveal deep insight into the genetic and molecular bases of human biology and disease. Undesired editing outcomes, both on-target (at the edited locus) and off-target (at other genomic loci) hinder the application of CRISPR-Cas nucleases. We developed Off-flow, a Nextflow-coded bioinformatic workflow that takes a specific guide sequence and Cas protein input to call four separate off-target prediction programs (CHOPCHOP, Cas-Offinder, CRISPRitz, CRISPR-Offinder) to output a comprehensive list of predicted off-target sites. We applied it to whole genome sequencing (WGS) data to investigate the occurrence of unintended effects in human iPSCs that underwent repair or insertion of disease-related variants by homology-directed repair. Off-flow identified a 3-base-pair-substitution and a mono-allelic genomic deletion at the target loci, KCNQ2, in 2 clones. Unbiased WGS analysis further identified off-target missense variants and a mono-allelic genomic deletion at the targeted locus, GNAQ, in 10 clones. On-target substitution and deletions had escaped standard PCR and Sanger sequencing analysis, while missense variants at other genomic loci were not detected by Off-flow. We used these results to filter out iPSC clones for subsequent functional experiments. Off-flow, which we make publicly available, works for human and mouse genomes currently and can be adapted for other genomes. Off-flow and WGS analysis can improve the integrity of studies using CRISPR/Cas-edited cells and animal models.

Comparing variants related to chronic diseases from genome-wide association study (GWAS) and the cancer genome atlas (TCGA)

BackgroundSeveral genome-wide association studies (GWAS) have been performed to identify variants related to chronic diseases. Somatic variants in cancer tissues are associated with cancer development and prognosis. Expression quantitative trait loci (eQTL) and methylation QTL (mQTL) analyses were performed on chronic disease-related variants in TCGA dataset.MethodsMuTect2 calling variants for 33 cancers from TCGA and 296 GWAS variants provided by LocusZoom were used. At least one mutation was found in TCGA 22 cancers and LocusZoom 23 studies. Differentially expressed genes (DEGs) and differentially methylated regions (DMRs) from the three cancers (TCGA-COAD, TCGA-STAD, and TCGA-UCEC). Variants were mapped to the world map using population locations of the 1000 Genomes Project (1GP) populations. Decision tree analysis was performed on the discovered features and survival analysis was performed according to the cluster.ResultsBased on the DEGs and DMRs with clinical data, the decision tree model classified seven and three nodes in TCGA-COAD and TCGA-STAD, respectively. A total of 11 variants were commonly detected from TCGA and LocusZoom, and eight variants were selected from the 1GP variants, and the distribution patterns were visualized on the world map.ConclusionsVariants related to tumors and chronic diseases were selected, and their geological regional 1GP-based proportions are presented. The variant distribution patterns could provide clues for regional clinical trial designs and personalized medicine.

Structural evolution of fibril polymorphs during amyloid assembly

Cryoelectron microscopy (cryo-EM) has provided unprecedented insights into amyloid fibril structures, including those associated with disease. However, these structures represent the endpoints of long assembly processes, and their relationship to fibrils formed early in assembly is unknown. Consequently, whether different fibril architectures, with potentially different pathological properties, form during assembly remains unknown. Here, we used cryo-EM to determine structures of amyloid fibrils at different times during invitro fibrillation of a disease-related variant of human islet amyloid polypeptide (IAPP-S20G). Strikingly, the fibrils formed in the lag, growth, and plateau phases have different structures, with new forms appearing and others disappearing as fibrillation proceeds. A time course with wild-type hIAPP also shows fibrils changing with time, suggesting that this is a general property of IAPP amyloid assembly. The observation of transiently populated fibril structures has implications for understanding amyloid assembly mechanisms with potential new insights into amyloid progression in disease.